CAREER: Advances in Modern Causal Inference: High Dimensions, Heterogeneity, and Beyond

因果关系是科学和政策中许多最重要问题的核心。哪种癌症治疗方法对哪种患者最好？监禁会阻碍或鼓励再犯吗？因果推理关注的是用数学的方法来表达这些问题，探索是否可以从数据中获得答案，如果可以，确定如何以及用什么方法。因果推理的经典设置探讨了简单干预措施的影响，并假设混淆关系足够简单，可以以相对低的误差进行估计。然而，简单的“全有或全无”效应可能掩盖了潜在的异质性，并且实际上可能不现实或几乎不可能估计，例如，如果一些受试者没有机会接受治疗。此外，在现代背景下，混杂因素往往是高维的，与未知的、可能非常复杂的暴露和结果有关。适应现实混淆和异质性是现代因果推理中两个最核心的挑战。这些追求产生了无数悬而未决的问题，从理解高维因果推理的基本限制到探索全新的效果。本项目旨在通过发展新的理论和方法，促进因果推理在公共政策和医学等领域的应用，帮助解决这些问题。外联也是一个重要组成部分。新软件将在r免费提供。PI将设计一门关于定量因果推理的本科课程，以帮助推动数据素养从关联到因果关系。将编写教科书，并有许多广泛参与的机会，包括暑期课程、讲习班和短期课程。该项目旨在开发新的理论和方法，用于研究更细微但实际的效果度量，以适应实践中经常发现的复杂数据结构。研究将集中在(1)高维混淆的调整和(2)异质治疗效果和最佳治疗方案的灵活估计。扩展也将追求多值时变暴露受到不可测量的混杂。对于(1)，PI旨在为经典效应和新的基于倾向的效应开发新的非渐近风险界，以及极小极大下界。这是在一个高维离散模型（新的因果推理）以及连续数据中完成的。对于(2)，PI计划在灵活的非参数模型中确定异质效应估计的基本限制，开发和分析新的异质效应估计器，并研究在新的“接触约束”下的最佳处理方案。这项工作有可能帮助改变我们对现代大数据时代因果推理的理解。这些项目还将直接促进社会学、犯罪学和医学的具体应用研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Causality is at the heart of many of the most important questions in science and policy. Which cancer treatments are best for which patients? Does incarceration impede or encourage recidivism? Causal inference is concerned with formulating such questions mathematically, exploring whether answers can be obtained from data, and if so, determining how well and with what methods. The classical setup in causal inference explores effects of simple interventions, and presumes confounding relationships are straightforward enough to estimate with relatively low error. However, simple "all-or-nothing" effects may mask underlying heterogeneity, and can be practically unrealistic or nearly impossible to estimate, for example if some subjects have no chance of receiving treatment. Further, in modern contexts, confounders are often high-dimensional and relate to exposures and outcomes in unknown and possibly very complex ways. Accommodating realistic confounding and heterogeneity are two of the most central challenges in modern causal inference. These pursuits yield myriad open questions, from understanding fundamental limits of causal inference in high dimensions to exploring entirely new effects altogether. This project aims to help address these questions by developing novel theory and methods and advancing the application of causal inference in fields such as public policy and medicine. Outreach is also a major component. New software will be made freely available in R. The PI will design an undergraduate course on quantitative causal reasoning, to help push data literacy forward from association to causation. A textbook will be written, and there will be numerous opportunities for broad participation, including summer programs, workshops, and short courses.This project aims to develop new theory and methods for the study of more nuanced - yet practical - effect measures, accommodating the complex data structures often found in practice. The research will focus on (1) adjustment for high-dimensional confounding and (2) flexible estimation of heterogeneous treatment effects and optimal treatment regimes. Extensions will also be pursued for multivalued time-varying exposures subject to unmeasured confounding. For (1), the PI aims to develop novel non-asymptotic risk bounds for both classical and new propensity-based effects, as well as minimax lower bounds. This is accomplished in a high-dimensional discrete model (new to causal inference) as well as with continuous data. For (2), the PI plans to determine the fundamental limits of heterogeneous effect estimation in flexible nonparametric models, develop and analyze novel heterogeneous effect estimators, and study optimal treatment regimes under novel "contact constraints." This work has the potential to help transform our understanding of causal inference in the modern big data era. The projects will also directly contribute to research on specific applications in sociology, criminology, and medicine.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.